Day 3 of #ProbAI School is underway!
Jonas Arruda on Simulation-Based Inference + @smaglia.bsky.social on Causal ML. Energy is high after a strong start. Thanks to diamond sponsor Vinted
& all partners! #ProbabilisticAI #SimulationBasedInference #CausalML
Jonas Arruda on Simulation-Based Inference + @smaglia.bsky.social on Causal ML. Energy is high after a strong start. Thanks to diamond sponsor Vinted
& all partners! #ProbabilisticAI #SimulationBasedInference #CausalML
August 5, 2026 at 12:59 PM
Day 3 of #ProbAI School is underway!
Jonas Arruda on Simulation-Based Inference + @smaglia.bsky.social on Causal ML. Energy is high after a strong start. Thanks to diamond sponsor Vinted
& all partners! #ProbabilisticAI #SimulationBasedInference #CausalML
Jonas Arruda on Simulation-Based Inference + @smaglia.bsky.social on Causal ML. Energy is high after a strong start. Thanks to diamond sponsor Vinted
& all partners! #ProbabilisticAI #SimulationBasedInference #CausalML
🎉New preprint: #CausalML to analyze effects of behavioral interventions
• We use #CausalML to learn when loss vs. gain framing works
• 2 field experiments (N=41,207) from retirement saving
• Personalization⬆️participation +51% & savings +18%
papers.ssrn.com/sol3/papers....
• We use #CausalML to learn when loss vs. gain framing works
• 2 field experiments (N=41,207) from retirement saving
• Personalization⬆️participation +51% & savings +18%
papers.ssrn.com/sol3/papers....
<p>Heterogeneous Effects of Loss Versus Gain Framing on Retirement Savings: Evidence from Two Randomized Field Experiments Using Causal Machine Learning</p>
As Western countries face rapidly aging populations, insufficient retirement savings threaten individual financial security and the sustainability of social wel
papers.ssrn.com
June 3, 2026 at 9:07 AM
🎉New preprint: #CausalML to analyze effects of behavioral interventions
• We use #CausalML to learn when loss vs. gain framing works
• 2 field experiments (N=41,207) from retirement saving
• Personalization⬆️participation +51% & savings +18%
papers.ssrn.com/sol3/papers....
• We use #CausalML to learn when loss vs. gain framing works
• 2 field experiments (N=41,207) from retirement saving
• Personalization⬆️participation +51% & savings +18%
papers.ssrn.com/sol3/papers....
🏥💊New paper on #CausalML in #oncology:
Treatment effect heterogeneity of radiotherapy in localized Ewing sarcoma: A secondary analysis of the EURO-E.W.I.N.G. 99 and Ewing 2008 trial
📄 doi.org/10.1016/j.ej...
Treatment effect heterogeneity of radiotherapy in localized Ewing sarcoma: A secondary analysis of the EURO-E.W.I.N.G. 99 and Ewing 2008 trial
📄 doi.org/10.1016/j.ej...
Redirecting
doi.org
May 28, 2026 at 12:48 PM
🏥💊New paper on #CausalML in #oncology:
Treatment effect heterogeneity of radiotherapy in localized Ewing sarcoma: A secondary analysis of the EURO-E.W.I.N.G. 99 and Ewing 2008 trial
📄 doi.org/10.1016/j.ej...
Treatment effect heterogeneity of radiotherapy in localized Ewing sarcoma: A secondary analysis of the EURO-E.W.I.N.G. 99 and Ewing 2008 trial
📄 doi.org/10.1016/j.ej...
May 28, 2026 at 5:47 AM
📢Checkout our new overview on #CausalML in Wiley #StatsRef
👉We give a concise overview of ML for causal inference — incl. IPTW, AIPTW, TMLE, meta-learners, Neyman orthogonality, ...
📄 doi.org/10.1002/9781... (or PM me)
with @larsvanderlaan3.bsky.social @valik-melnychuk.bsky.social
👉We give a concise overview of ML for causal inference — incl. IPTW, AIPTW, TMLE, meta-learners, Neyman orthogonality, ...
📄 doi.org/10.1002/9781... (or PM me)
with @larsvanderlaan3.bsky.social @valik-melnychuk.bsky.social
May 27, 2026 at 5:23 PM
📢Checkout our new overview on #CausalML in Wiley #StatsRef
👉We give a concise overview of ML for causal inference — incl. IPTW, AIPTW, TMLE, meta-learners, Neyman orthogonality, ...
📄 doi.org/10.1002/9781... (or PM me)
with @larsvanderlaan3.bsky.social @valik-melnychuk.bsky.social
👉We give a concise overview of ML for causal inference — incl. IPTW, AIPTW, TMLE, meta-learners, Neyman orthogonality, ...
📄 doi.org/10.1002/9781... (or PM me)
with @larsvanderlaan3.bsky.social @valik-melnychuk.bsky.social
CausalML uplift modeling treatment effects Python
via Medium Python
https://flarestart.com/article/causalml-uplift-modeling-treatment-effects-python-20260401
#DevNews #ProgrammingLanguages
via Medium Python
https://flarestart.com/article/causalml-uplift-modeling-treatment-effects-python-20260401
#DevNews #ProgrammingLanguages
CausalML uplift modeling treatment effects Python
Imagine this result from a marketing experiment: Continue reading on Medium »
flarestart.com
April 1, 2026 at 3:54 AM
CausalML uplift modeling treatment effects Python
via Medium Python
https://flarestart.com/article/causalml-uplift-modeling-treatment-effects-python-20260401
#DevNews #ProgrammingLanguages
via Medium Python
https://flarestart.com/article/causalml-uplift-modeling-treatment-effects-python-20260401
#DevNews #ProgrammingLanguages
Prediction is useful. Understanding *why* things happen is powerful. Causal machine learning helps move beyond correlation to real insight, supporting smarter, more ethical decisions across industries. #CausalML #MachineLearning #DataScience #ExplainableAI #DecisionScience
taxodiary.com
February 25, 2026 at 9:02 AM
Prediction is useful. Understanding *why* things happen is powerful. Causal machine learning helps move beyond correlation to real insight, supporting smarter, more ethical decisions across industries. #CausalML #MachineLearning #DataScience #ExplainableAI #DecisionScience
February 9, 2026 at 9:46 PM
Excited to share our new paper in Psychological Methods: “Machine Learning for Propensity Score Estimation: A Systematic Review and Reporting Guidelines.” Led by Prof. Walter Leite (UF) with 6 co-authors across 6 universities. DOI: doi.org/10.1037/met0...
#CausalML #PropensityScore
#CausalML #PropensityScore
APA PsycNet
doi.org
October 20, 2025 at 4:18 PM
Excited to share our new paper in Psychological Methods: “Machine Learning for Propensity Score Estimation: A Systematic Review and Reporting Guidelines.” Led by Prof. Walter Leite (UF) with 6 co-authors across 6 universities. DOI: doi.org/10.1037/met0...
#CausalML #PropensityScore
#CausalML #PropensityScore
New AI framework X-MultiTask achieves a top AUC of 0.84 for anterior spinal-fusion and 0.77 for posterior, with lowest ε_nn-PEHE of 0.2778. Read more: https://getnews.me/ai-framework-enhances-causal-machine-learning-for-surgical-treatment-effects/ #causalml #surgery
September 26, 2025 at 4:21 PM
New AI framework X-MultiTask achieves a top AUC of 0.84 for anterior spinal-fusion and 0.77 for posterior, with lowest ε_nn-PEHE of 0.2778. Read more: https://getnews.me/ai-framework-enhances-causal-machine-learning-for-surgical-treatment-effects/ #causalml #surgery
Excited to join the Impact 25 workshop at #EurIPS2025 in Copenhagen, Dec 6–7! 🌍✨
We'll explore how to boost the real-world impact of causal ML, representation learning, discovery & inference across health, social & earth sciences.
👉 impact-25.causal.dev
#CausalInference #CausalML #Impact25
We'll explore how to boost the real-world impact of causal ML, representation learning, discovery & inference across health, social & earth sciences.
👉 impact-25.causal.dev
#CausalInference #CausalML #Impact25
September 22, 2025 at 8:50 AM
Excited to join the Impact 25 workshop at #EurIPS2025 in Copenhagen, Dec 6–7! 🌍✨
We'll explore how to boost the real-world impact of causal ML, representation learning, discovery & inference across health, social & earth sciences.
👉 impact-25.causal.dev
#CausalInference #CausalML #Impact25
We'll explore how to boost the real-world impact of causal ML, representation learning, discovery & inference across health, social & earth sciences.
👉 impact-25.causal.dev
#CausalInference #CausalML #Impact25
The new Causal‑Symbolic Meta‑Learning (CSML) framework learns causal graphs from few examples and outperformed baselines on the CausalWorld benchmark. Read more: https://getnews.me/causal-symbolic-meta-learning-enables-few-shot-causal-reasoning/ #causalml #meta-learning
September 18, 2025 at 5:49 AM
The new Causal‑Symbolic Meta‑Learning (CSML) framework learns causal graphs from few examples and outperformed baselines on the CausalWorld benchmark. Read more: https://getnews.me/causal-symbolic-meta-learning-enables-few-shot-causal-reasoning/ #causalml #meta-learning
the original paper repo github.com/CausalML/VMM
and this related paper repo github.com/HeinerKremer...
have pretty solid implementations imo. More generally, since VMM is an ERM problem, I think you could use any general optimization for it?
and this related paper repo github.com/HeinerKremer...
have pretty solid implementations imo. More generally, since VMM is an ERM problem, I think you could use any general optimization for it?
GitHub - HeinerKremer/conditional-moment-restrictions: Estimators for conditional moment restriction problems
Estimators for conditional moment restriction problems - HeinerKremer/conditional-moment-restrictions
github.com
September 16, 2025 at 3:53 PM
the original paper repo github.com/CausalML/VMM
and this related paper repo github.com/HeinerKremer...
have pretty solid implementations imo. More generally, since VMM is an ERM problem, I think you could use any general optimization for it?
and this related paper repo github.com/HeinerKremer...
have pretty solid implementations imo. More generally, since VMM is an ERM problem, I think you could use any general optimization for it?
difficult to validate and built on untestable assumptions about the causal structure, confounds etc. Adding more covariates doesn't necessarily help. See "The Good, the Bad, and the Ugly" section on CausalML here arxiv.org/pdf/2206.15475
arxiv.org
August 20, 2025 at 2:51 PM
difficult to validate and built on untestable assumptions about the causal structure, confounds etc. Adding more covariates doesn't necessarily help. See "The Good, the Bad, and the Ugly" section on CausalML here arxiv.org/pdf/2206.15475
This has become my latest pet peeve. "Causal" is sexy and creates the impression that you're uncovering mechanism(s), but "causal estimates" are often just associational and/or reliant on strong assumptions that are rarely met.
E.g., CausalML applied to observational data. Cool, but...
E.g., CausalML applied to observational data. Cool, but...
However, AI folk, it is going to be far, far better if we collectively call "back in time" or "left" in the normal way to lay out time as "antecedent" and *not* causal.
August 20, 2025 at 2:51 PM
This has become my latest pet peeve. "Causal" is sexy and creates the impression that you're uncovering mechanism(s), but "causal estimates" are often just associational and/or reliant on strong assumptions that are rarely met.
E.g., CausalML applied to observational data. Cool, but...
E.g., CausalML applied to observational data. Cool, but...
🚨 Hiring a postdoc @ucdavis.bsky.social GSM!
Work on causal inference + econometrics + ML:
- Heterogeneous effects
- Dynamic interventions
- Censoring & noncompliance
Strong stats theory + R/Python
Start: Fall 2025
Apply → recruit.ucdavis.edu/JPF07156
#CausalML #EconSky #PostdocJobs
Work on causal inference + econometrics + ML:
- Heterogeneous effects
- Dynamic interventions
- Censoring & noncompliance
Strong stats theory + R/Python
Start: Fall 2025
Apply → recruit.ucdavis.edu/JPF07156
#CausalML #EconSky #PostdocJobs
Postdoctoral Position- UC Davis Graduate School of Management
University of California, Davis is hiring. Apply now!
recruit.ucdavis.edu
July 20, 2025 at 9:26 AM
🚨 Hiring a postdoc @ucdavis.bsky.social GSM!
Work on causal inference + econometrics + ML:
- Heterogeneous effects
- Dynamic interventions
- Censoring & noncompliance
Strong stats theory + R/Python
Start: Fall 2025
Apply → recruit.ucdavis.edu/JPF07156
#CausalML #EconSky #PostdocJobs
Work on causal inference + econometrics + ML:
- Heterogeneous effects
- Dynamic interventions
- Censoring & noncompliance
Strong stats theory + R/Python
Start: Fall 2025
Apply → recruit.ucdavis.edu/JPF07156
#CausalML #EconSky #PostdocJobs
Causal machine learning for assessing the effectiveness of off-label use of amiodarone in new-onset atrial fibrillation
Feuerriegel, S., Ghanbari, H. et al.
Paper
Details
#CausalML #OffLabelAmiodarone #AFibTreatment
Feuerriegel, S., Ghanbari, H. et al.
Paper
Details
#CausalML #OffLabelAmiodarone #AFibTreatment
June 27, 2025 at 9:02 AM
Causal machine learning for assessing the effectiveness of off-label use of amiodarone in new-onset atrial fibrillation
Feuerriegel, S., Ghanbari, H. et al.
Paper
Details
#CausalML #OffLabelAmiodarone #AFibTreatment
Feuerriegel, S., Ghanbari, H. et al.
Paper
Details
#CausalML #OffLabelAmiodarone #AFibTreatment
Grateful to present recent research @SFU International Conference in Statistics & Data Science 2025 Simon Fraser University in Vancouver on #TargetTrialEmulation using electronic health records to estimate average treatment effects & #CausalML conditional average treatment effects
June 26, 2025 at 1:40 AM
Grateful to present recent research @SFU International Conference in Statistics & Data Science 2025 Simon Fraser University in Vancouver on #TargetTrialEmulation using electronic health records to estimate average treatment effects & #CausalML conditional average treatment effects
🚨 Call for Papers: Causal Data Science Meeting 2025
📅 November 12–13, 2025 (Virtual)
📥 Submit by Sept 30: submission@causalscience.org
🎙️ Keynote: Stefan Feuerriegel (LMU Munich)
🌐 More Info and registration: causalscience.org
#CausalML #AI #DataScience #CDSM2025 #CanIPetThatDAG
📅 November 12–13, 2025 (Virtual)
📥 Submit by Sept 30: submission@causalscience.org
🎙️ Keynote: Stefan Feuerriegel (LMU Munich)
🌐 More Info and registration: causalscience.org
#CausalML #AI #DataScience #CDSM2025 #CanIPetThatDAG
June 6, 2025 at 1:54 PM
🚨 Call for Papers: Causal Data Science Meeting 2025
📅 November 12–13, 2025 (Virtual)
📥 Submit by Sept 30: submission@causalscience.org
🎙️ Keynote: Stefan Feuerriegel (LMU Munich)
🌐 More Info and registration: causalscience.org
#CausalML #AI #DataScience #CDSM2025 #CanIPetThatDAG
📅 November 12–13, 2025 (Virtual)
📥 Submit by Sept 30: submission@causalscience.org
🎙️ Keynote: Stefan Feuerriegel (LMU Munich)
🌐 More Info and registration: causalscience.org
#CausalML #AI #DataScience #CDSM2025 #CanIPetThatDAG
Bookmark our lab page and GitHub repo to follow our work:
muratkocaoglu.com/CausalML/
github.com/CausalML-Lab
muratkocaoglu.com/CausalML/
github.com/CausalML-Lab
CausalML Lab
muratkocaoglu.com
June 3, 2025 at 8:42 AM
Bookmark our lab page and GitHub repo to follow our work:
muratkocaoglu.com/CausalML/
github.com/CausalML-Lab
muratkocaoglu.com/CausalML/
github.com/CausalML-Lab
CausalML Lab will continue to push the boundaries of fundamental causal inference and discovery research with an added focus on real-world applications and impact. If you are at Johns Hopkins @jhu.edu, or more generally on the East Coast, and are interested in collaborating, please reach out.
June 3, 2025 at 8:42 AM
CausalML Lab will continue to push the boundaries of fundamental causal inference and discovery research with an added focus on real-world applications and impact. If you are at Johns Hopkins @jhu.edu, or more generally on the East Coast, and are interested in collaborating, please reach out.